Abstract
Gas sensor drift affects the performance of chemical sensing. In this paper, a Long Short Term Memory (LSTM) network and a Support Vector Machine (SVM) are used for gas sensor drift compensation to improve gas classification performance. An improved dynamic feature extraction method is developed to reduce feature dimensions. A public time series chemical sensing dataset is used for evaluation, which was collected by 16 metal-oxide gas sensors over three years. Results show that a high classfication accuracy can be achieved using the proposed method compared to other studies, which demonstrates the robustness of the proposed method for sensor drift compensation.
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Acknowledgments
This research was partially supported by the National Natural Science Foundation of China under Grant 61976063, the funding of Overseas 100 Talents Program of Guangxi Higher Education under Grant F-KA16035, the Diecai Project of Guangxi Normal University, 2018 Guangxi One Thousand Young and Middle-Aged College and University Backbone Teachers Cultivation Program, research fund of Guangxi Key Lab of Multi-source Information Mining & Security (19-A-03-02), research fund of Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, the Young and Middle-aged Teachers’ Research Ability Improvement Project in Guangxi Universities under Grant 2020KY02030, and the Innovation Project of Guangxi Graduate Education under Grant YCSW2020102.
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Wu, G., Liu, J., Luo, Y., Qiu, S. (2020). Sensor Drift Compensation Using Robust Classification Method. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Lecture Notes in Computer Science(), vol 12534. Springer, Cham. https://doi.org/10.1007/978-3-030-63836-8_50
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DOI: https://doi.org/10.1007/978-3-030-63836-8_50
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